📺 ML YouTube Courses
At dair.ai we
- Stanford CS229: Machine Learning
- Applied Machine Learning
- Machine Learning with Graphs (Stanford)
- Probabilistic Machine Learning
- Deep Learning: CS 182
- Deep Unsupervised Learning
- NYU Deep Learning SP21
- CMU Neural Networks for NLP
- Multilingual NLP
- Advanced NLP
- Deep Learning for Computer Vision
- Deep Reinforcement Learning
- Full Stack Deep Learning
Stanford CS229: Machine Learning
To learn some of the basics of ML:
• Linear Regression and Gradient Descent • Logistic Regression • Naive Bayes • SVMs • Kernels • Decision Trees • Introduction to Neural Networks • Debugging ML Models ...
Applied Machine Learning
To learn some of the most widely used techniques in ML:
• Optimization and Calculus • Overfitting and Underfitting • Regularization • Monte Carlo Estimation • Maximum Likelihood Learning • Nearest Neighbours ...
Machine Learning with Graphs (Stanford)
To learn some of the latest graph techniques in machine learning:
• PageRank • Matrix Factorizing • Node Embeddings • Graph Neural Networks • Knowledge Graphs • Deep Generative Models for Graphs ...
Probabilistic Machine Learning
To learn the probabilistic paradigm of ML:
• Reasoning about uncertainty • Continuous Variables • Sampling • Markov Chain Monte Carlo • Gaussian Distributions • Graphical Models • Tuning Inference Algorithms ...
Deep Learning: CS 182
To learn some of the widely used techniques in deep learning:
• Machine Learning Basics • Error Analysis • Optimization • Backpropagation • Initialization • Batch Normalization • Style transfer • Imitation Learning ...
Deep Unsupervised Learning
To learn the latest and most widely used techniques in deep unsupervised learning:
• Autoregressive Models • Flow Models • Latent Variable Models • Self-supervised learning • Implicit Models • Compression ...
NYU Deep Learning SP21
To learn some of the advanced techniques in deep learning:
• Neural Nets: rotation and squashing • Latent Variable Energy Based Models • Unsupervised Learning • Generative Adversarial Networks • Autoencoders ...
CMU Neural Networks for NLP
To learn the latest neural network based techniques for NLP: • Language Modeling • Efficiency tricks • Conditioned Generation • Structured Prediction • Model Interpretation • Advanced Search Algorithms ...
To learn the latest concepts for doing multilingual NLP:
• Typology • Words, Part of Speech, and Morphology • Advanced Text Classification • Machine Translation • Data Augmentation for MT • Low Resource ASR • Active Learning ...
To learn advanced concepts in NLP:
• Attention Mechanisms • Transformers • BERT • Question Answering • Model Distillation • Vision + Language • Ethics in NLP • Commonsense Reasoning ...
Deep Learning for Computer Vision
To learn some of the fundamental concepts in CV:
• Introduction to deep learning for CV • Image Classification • Convolutional Networks • Attention Networks • Detection and Segmentation • Generative Models ...
Deep Reinforcement Learning
To learn the latest concepts in deep RL:
• Intro to RL • RL algorithms • Real-world sequential decision making • Supervised learning of behaviors • Deep imitation learning • Cost functions and reward functions ...
Full Stack Deep Learning
To learn full-stack production deep learning:
• ML Projects • Infrastructure and Tooling • Experiment Managing • Troubleshooting DNNs • Data Management • Data Labeling • Monitoring ML Models • Web deployment ...
There are many plans to keep improving this collection. For instance, I will be sharing notes and better organizing individual lectures in a way that provides a bit of guidance for those that are getting started with machine learning.
If you are interested to contribute, feel free to open a PR with links to all individual lectures for each course. It will take a bit of time, but I have plans to do many things with these individual lectures. We can summarize the lectures, include notes, provide additional reading material, include difficulty of content, etc.